Digital Rock

Digital rock physics utilizes advanced imaging techniques, primarily X-ray computed tomography (CT) and scanning electron microscopy (SEM), to create detailed 3D models of rock samples. Current research focuses on applying and improving deep learning methods, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs), for tasks like image segmentation, classification, and property prediction (e.g., porosity, permeability, elastic moduli). These advancements enable more efficient and accurate characterization of rock properties, impacting fields like reservoir modeling, planetary science, and material engineering by providing detailed, quantitative insights into rock microstructure and its relationship to macroscopic behavior.